Waiting endlessly for a bus
to arrive may soon be a thing of the past — a method for analyzing
the flow of passengers on public bus networks, developed by researchers at
A*STAR, could enable schedule optimization and minimize waiting times.

Global positioning systems have long enabled real-time
estimations of the arrival time of buses on many of the world’s
public transport networks. When this automated vehicle location (AVL) data is
combined with passenger information — known as automated passenger collection (APC) data and usually collected as the passenger scans their tickets — an intricate
picture of human motion across a metropolitan area can be compiled. This
information, in turn, can be harnessed to improve public transport.

One useful metric by which public transport systems
are assessed is the average time a commuter must wait for their bus. Waiting
times can be recorded manually or by video, but these methods are intrinsically
very limited in scale. Instead, researchers have developed mathematical
formulae that try to model average waiting times from the so-called ‘headway
time’, the time until the next bus, based on AVL data. But this approach
requires several unverified assumptions. For example, it assumes that for high
frequency services, commuters arrive at the bus stop uniformly.

Muhamad Azfar Ramli from A*STAR’s Institute of High
Performance Computing and collaborators from a local bus company have analysed
AVL and APC data from ten bus routes in Singapore over one month. They showed
that the correlation between the number of commuters boarding and the
corresponding headway is poor, thus indicating that the uniform arrival
assumption is incorrect in Singapore. The team then used this data to build an
accurate simulation, which was able to accurately reproduce the empirical data
gathered in Singapore in August 2014.

With the accuracy and efficiency of their model
confirmed, the researchers were able to use the technique to propose schedules
that would minimize the average commuter waiting time without necessarily
imposing more buses on the network.

“We hope that our work lays the foundation for more
dynamic and responsive transport operations that can be reactive not only to
changes in bus locations and movements but also to commuting demand,” says
Ramli. At present, the analysis can only be done a day or two after data
collection, but the team believes this can be done faster. “We hope that when both our estimation
techniques and the available real-time data streaming technologies have
improved, our methodologies can be applied in real-time and enable operators to
react more accurately to dynamic changes in service quality.”